Marwan Alshar'e

@su.edu.om

Sohar University

Marwan Alshar'e


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26

Scopus Publications

508

Scholar Citations

13

Scholar h-index

18

Scholar i10-index

Scopus Publications

  • DISCOVERING LEARNER PERSONAS IN AI-ASSISTED ENGLISH LANGUAGE LEARNING USING COSINE-BASED CLUSTERING: IMPLICATIONS FOR PERSONALIZED SUPPORT IN GCC CONTEXTS
    Bangladesh Journal of Multidisciplinary Scientific Research, 2026
    The rapid expansion of artificial intelligence (AI)-assisted English language learning tools has introduced substantial variability in learner outcomes due to differences in behavioural patterns, task engagement, and usage strategies among non-native learners, particularly within Omani educational contexts. This heterogeneity creates a methodological challenge in identifying consistent learner profiles without relying on predefined or subjective labels. This study investigates the effectiveness of unsupervised cosine-based clustering in identifying distinct learner personas in AI-assisted English learning environments. The study utilizes a dataset of 15,000 learner interaction records obtained from Kaggle, incorporating demographic attributes, behavioural features, task modalities, and learning outcome indicators. A structured experimental methodology is employed, beginning with baseline Euclidean K-Means clustering, followed by dimensionality reduction using Singular Value Decomposition (SVD), and subsequent clustering using cosine similarity across multiple algorithms, including K-Means, Gaussian Mixture Models, Agglomerative Clustering, and BIRCH. The results reveal that cosine-based K-Means clustering (k = 6) achieves a Silhouette Score of 0.678 compared to 0.10 for baseline Euclidean clustering, representing an absolute improvement of 0.578 and approximately a sixfold increase in clustering performance. Compared to SVD-based Euclidean clustering (Silhouette = 0.41), cosine similarity improves clustering effectiveness by approximately 65%, while the Davies–Bouldin Index decreases to 0.56 and the Calinski–Harabasz Index increases to 33,074. The findings indicate that cosine-based unsupervised modelling effectively identifies distinct learner personas, demonstrating that learning-gain variations are driven by behavioural interaction patterns rather than usage intensity alone.
  • EDUCATIONAL AI FOR ATTENTION-ENHANCED FACIAL EMOTION RECOGNITION FOR EMOTION-AWARE LEARNING SYSTEMS USING FACE-CROPPED DEEP NETWORKS
    Bangladesh Journal of Multidisciplinary Scientific Research, 2026
    Emotion-aware learning systems depend on reliable recognition of students' affective states. However, facial emotion recognition in child-centred settings remains difficult due to background clutter, class imbalance, limited annotated data, and subtle variations in facial expressions. This study investigates whether a face-centric, attention-enhanced deep learning framework can improve recognition performance and convergence efficiency for educational artificial intelligence applications. The study employs the Multimodal Child Emotion for Learning dataset and implements a pipeline that uses Multi-Task Cascaded Convolutional Networks to detect and crop faces, an EfficientNet-B0 backbone to learn facial features, an Efficient Channel Attention module to strengthen discriminative channel representations, and a staged training procedure involving classifier-head training followed by full-network fine-tuning; experiments are conducted in PyTorch with ImageNet-pretrained weights, Adam optimization, cross-entropy loss, and augmentation through horizontal flipping and colour jittering. The results show that face detection successfully localised 826 of 833 images, baseline validation accuracy improved from approximately 14% to 68% after integrating MTCNN-based face cropping and ECA, and the final proposed configuration reached 77.78% validation accuracy after excluding the severely underrepresented fear class. Staged training achieved the 0.60 validation-accuracy threshold in 6 epochs rather than 10 and reduced total training time from 22.79 to 20.02 minutes, equivalent to a 12.15% reduction. Ablation analysis showed that validation accuracy declined by 9.8% without face cropping, 6.2% without channel attention, and 4.1% without staged training. The findings quantitatively demonstrate that the combined framework improves recognition reliability, accelerates convergence, and achieves the strongest performance across all tested configurations in the evaluated educational dataset.
  • A BUSINESS INTELLIGENCE SIMULATOR FOR SALES OPTIMIZATION USING A MACHINE LEARNING DDS FRAMEWORK
    Bangladesh Journal of Multidisciplinary Scientific Research, 2026
    Existing business intelligence (BI) systems are predominantly descriptive and offer limited support for actionable decision-making in sales optimization, leading to a disconnect between data analytics and operational management. This study examines the development of a unified BI simulator that integrates machine learning, Pareto-based diagnostics, and what-if simulation to support data-driven sales optimization. The research employs an empirical pizza sales dataset from Kaggle, consisting of transactional, temporal, and operational variables. It applies multiple machine learning algorithms for comparative evaluation, along with Pareto analysis to identify key product contributors and a simulation engine to assess the sensitivity of low-performing products to operational changes. The results reveal that the Random Forest model outperforms other models, achieving 97.5% accuracy, an F1-score of 0.941, and an AUC of 0.996. Pareto analysis shows that approximately 30% of product categories account for nearly 80% of total sales, while a small proportion of products consistently exhibit low demand. Additionally, simulation analysis indicates that variations in operational factors, such as delivery efficiency, delivery distance, and product complexity, result in sales variability of up to 12.8%. The findings of this study suggest that the integration of predictive, diagnostic, and simulation-based analytics within a unified BI framework enables precise identification of key sales drivers and quantitatively measures the responsiveness of low-performing products to operational changes, thereby offering a comprehensive and data-driven evaluation of sales performance.
  • QUANTIFYING THE VULNERABILITY OF CREDIT CARD FRAUD DETECTION SYSTEMS TO GRADIENT BASED ADVERSARIAL EVASION
    Bangladesh Journal of Multidisciplinary Scientific Research, 2026
    In financial cybersecurity, fraud detection is at the forefront of the battle against cybercriminals and deep learning models are the state-of-the-art method for real-time detection; yet their reliance on fixed feature distributions renders them inherently susceptible to adversarial attacks. With the rise of generative artificial intelligence (AI) in obfuscating fraudulent transactions, conventional performance indicators (accuracy and F1-score) do not reflect the cybersecurity resilience of these models. This research explores the Credit Card Fraud Detection dataset, comprising more than 550,000 anonymized records. The study uses a functional deep learning model as a benchmark and introduces the Fast Gradient Method (FGM) to simulate white box attacks on the latent features . The experiment results demonstrate a high detection accuracy of 99.85% under normal operating conditions, which dramatically reduces to 92.75% when card transactions are slightly modified by adversarial disturbances at an epsilon value of 0.1. Numerical evidence reveals a clear 7.10% "Security Gap," depicting a substantial degree of vulnerability, where transactions are mathematically reclassified from fraudulent to legitimate. Notably, our research suggests a non-linear "safety cliff" in the decrease of detection accuracy, where the model's reliability completely fails as the adversarial strength reaches 0.15 or higher. In addition, Principal Component Analysis demonstrates that gradient attacks have successfully distorted the latent features of fraudulent transactions, resulting in the movement of highly suspicious transactions towards the concentrated area of legitimate transactions, hence evading automated security screening while preserving the overall statistical distribution of the data.
  • Design and Development of SLRAI Wizards as an AI-Powered Solution to Enhance Learning Experiences in the Research Process
    Raja Muhammad Khairuddin Raja Rosli, Anies Faziehan Zakaria, Abdallah M. Abualkishik, Marwan Alshar’e, and
    Jurnal Kejuruteraan, 2025
    Conducting systematic literature reviews (SLRs) is an essential but demanding task, particularly for students and early-career researchers coping with complex methodologies and expanding bodies of literature. While artificial intelligence (AI) and large language models provide promising assistance, existing tools remain fragmented, difficult to use, and not tailored to the full SLR process. This study introduces SLRAI Wizards, a ChatGPT-based platform designed to guide researchers through planning, conducting, and reporting SLRs using established frameworks such as PRISMA, PRESS, CASP, and MMAT. The platform offers three engagement modes, self-paced, guided, and hybrid, supported by interactive diagrams, embedded tools, and tutorials. A preliminary evaluation was conducted with 41 participants in a two-hour guided online workshop. Survey data indicated strong positive perceptions: participants rated overall satisfaction at M = 4.33 (SD = 0.94) and reported increased confidence in conducting SLRs using AI tools (M = 4.42, SD = 0.64). The tool’s contribution to understanding the SLR process was highly rated (M = 4.67, SD = 0.62). Thematic analysis of open-ended responses highlighted four areas of impact: structured research guidance, efficiency gains, enhanced research skills, and motivational support. Users valued features such as the Screening Wizard, which allowed rapid abstract processing, but also called for clearer tutorials and a more intuitive interface. These findings suggest that SLRAI Wizards can lower entry barriers to SLRs, making them more accessible, rigorous, and engaging for novices. As a formative study, results establish feasibility and inform design refinements, paving the way for larger comparative evaluations across learning pathways.
  • Elliptic curve cryptography based light weight technique for information security
    Marwan Alshar’e, Sharf Alzu’bi, Ahed Al-Haraizah, Hamzah Ali Alkhazaleh, Malik Jawarneh, et al.
    Bulletin of Electrical Engineering and Informatics, 2025
    Recent breakthroughs in cryptographic technology are being thoroughly scrutinized due to their emphasis on innovative approaches to design, implementation, and attacks. Lightweight cryptography (LWC) is a technological advancement that utilizes a cryptographic algorithm capable of being adjusted to function effectively in various constrained environments. This study provides an in-depth analysis of elliptic curve cryptography (ECC), which is a type of asymmetric cryptographic method known as LWC. This cryptographic approach operates over elliptic curves and has two applications: key exchange and digital signature authentication. Next, we will implement asymmetric cryptographic algorithms and evaluate their efficiency. Elliptic curve elgamal algorithms are implemented for encryption and decryption of data. Elliptic curve Diffie-Hellman key exchange is used for sharing keys. Experimental results have shown that ECC needs small size keys to provide similar security. ECC takes less time in key generation, encryption and decryption of plain text. Time taken by ECC to generate a 2,048 bit long key is 1,653 milliseconds in comparison to 4,258 millisecond taken by Rivest-Shamir-Adleman (RSA) technique.
  • Accurate prediction of chronic diseases using deep learning algorithms
    Ronald S. Cordova, Rolou Lyn R. Maata, Malik Jawarneh, Marwan I. Alshar'e, Oliver C. Agustin
    Iaes International Journal of Artificial Intelligence, 2025
    In this paper, the researchers studied the effects of different activation functions in hidden layers and how they impact the overfitting or underfitting of the model in the multiclass prediction of chronic diseases. This paper also evaluated the effects of varying the number of layers, and hyperparameters and its impact on the accuracy of the model and its generalization capabilities. It was found that exponential linear unit (ELU) does not have a significant advantage over rectified linear unit (ReLU) when used as an activation function in the hidden layer. Additionally, the performance of softmax function, when used in the output layer, is the same as a classic sigmoid output activation function. In terms of the ability of the model to generalize, the researchers achieved a classification accuracy of 100% when the trained model was used to predict unseen data. Through this research, the researchers should be able to assist medical professionals and practitioners in Oman in the validation and diagnosis of chronic diseases in clinics and hospitals.
  • Classification of threats and countermeasures of cloud computing
    Rasha Almanasir, Deyaa Al-solomon, Saif Indrawes, Mohammed Amin Almaiah, Umar Islam, et al.
    Journal of Cyber Security and Risk Auditing, 2025
    This article focuses on the study of cloud computing, it’s various models, and cloud service types such as SaaS, PaaS, and IaaS. It emphasizes the security challenges and cyber threats associated with cloud environments, while also proposing methods and solutions to protect these systems. The study underlines the advantages of cloud computing in offering rapid, cost-effective access to technology and services, but also points out the vulnerabilities of multi-tenant architectures and the need for robust security measures to address these risks. Additionally, the article presents a detailed analysis of major security threats such as data loss, forgery, man-in-the-middle attacks, and denial of service (DoS) attacks—and explores detection and prevention techniques. These include the use of advanced tools for threat monitoring and pattern analysis, aimed at strengthening security and boosting user trust in cloud computing systems.
  • Assessing Blockchain's Role in Healthcare Security: A Comprehensive Review
    Marwan Alshar'e, Khaled Abuhmaidan, Falah Y. H. Ahmed, Abdallah Abualkishik, Mahmood Al-Bahri, et al.
    Informatica Slovenia, 2024
  • Enhancing Data Protection in Digital Communication: A Novel Method of Combining Steganography and Encryption
    Ksii Transactions on Internet and Information Systems, 2024
  • Enhancing IoT Network Security Through Digital Object Architecture-Based Approaches
    Mahmood Al-Bahri, Wasin Alkishri, Falah Y. H. Ahmed, Marwan Alshar'e, Sanad Al Maskari
    Qubahan Academic Journal, 2024
  • Smart In-Cabin Air Monitoring System using IoT Technologies
    Falah Y H Ahmed, Jabar H. Yousif, Marwan Alshar’e, Maram El Sheikh, Ehsan Al-Ajmi, et al.
    Qubahan Academic Journal, 2024
  • Enhancing Project Security: Unveiling Trust, Interpretability and Explainability in the Age of AI
    Marwan Alshar'e, Abdallah Abualkishik, Khaled Abuhmaidan, Ahmad Kayed
    5g Enabled Technology for Smart City and Urbanization System, 2024
  • Empowering the Future: Digital Twins and Connectivity in Smart City Development
    Abdallah Abualkishik, Marwan Alshar'e, Ahmad Kayed, Khaled Abuhmaidan, Ala Odeibat
    5g Enabled Technology for Smart City and Urbanization System, 2024
  • An Adaptive Two-Factor Authentication Scheme Based on the Usage of Schnorr Signcryption Algorithm
    Hussein Albazar, Ahmed Abdel-Wahab, Marwan Alshar'e, Abdallah Abualkishik
    Informatica Slovenia, 2023
  • Intelligent Gesture Recognition System for Deaf People by using CNN and IoT
    International Journal of Advances in Soft Computing and Its Applications, 2023
  • The Impact of Virtual Reality Technology on Jordan's Learning Environment and Medical Informatics among Physicians
    Malik Jawarneh, Marwan Alshar'e, Deshinta Arrova Dewi, Mohammad Al Nasar, Rasha Almajed, et al.
    International Journal of Computer Games Technology, 2023
  • Usability Evaluation of Educational Games: An Analysis of Culture as a Factor Affecting Children's Educational Attainment
    Marwan Alshar’e, Ali Albadi, Malik Jawarneh, Noman Tahir, Marya Al Amri
    Advances in Human Computer Interaction, 2022
  • A Framework of the Training Module for Untrained Observers in Usability Evaluation Motivated by COVID-19: Enhancing the Validity of Usability Evaluation for Children's Educational Games
    Marwan Alshar’e, Ali Albadi, Malik Mustafa, Noman Tahir, Marya Al Amri
    Advances in Human Computer Interaction, 2022
  • A Face Recognition Method In Machine Learning (ML) For Enhancing Security In Smart Home
    Marwan Alshar'e, Mohammad Rustom Al Nasar, Rohit Kumar, Manish Sharma, Dharamvir, et al.
    2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022
  • RETRACTED: Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems
    Malik Mustafa, Marwan Alshare, Deepshikha Bhargava, Rahul Neware, Balbir Singh, et al.
    Computational and Mathematical Methods in Medicine, 2022
  • Efficient Statistical Learning Framework with Applications to Human Activity and Facial Expression Recognition
    Fatma Najar, Sami Bourouis, Marwan Alshar'e, Roobaea Alroobaea, Nizar Bouguila, et al.
    2020 International Conference on Advanced Technologies for Signal and Image Processing Atsip 2020, 2020
  • The Moderating Effect of Demographic Factors Acceptance Virtual Reality Learning in Developing Countries in the Middle East
    Malik Mustafa, Sharf Alzubi, Marwan Alshare
    Communications in Computer and Information Science, 2020
  • Evaluation of the TPM user authentication model for trusted computers
    Journal of Theoretical and Applied Information Technology, 2015
  • Design and implementation of the TPM user authentication model
    Marwan Ibrahim Alshar’e, Rossilawati Sulaiman, Mohd Rosmadi Mokhtar, Abdullah MohdZin
    Journal of Computer Science, 2014
  • A user protection model for the trusted computing environment
    Alshar’e
    Journal of Computer Science, 2014

RECENT SCHOLAR PUBLICATIONS

  • A BUSINESS INTELLIGENCE SIMULATOR FOR SALES OPTIMIZATION USING A MACHINE LEARNING DDS FRAMEWORK
    NE Kadhi, MF Sadriwala, KF Sadriwala, RO Shannak, MA e
    Bangladesh Journal of Multidisciplinary Scientific Research 11 (2), 128-136 , 2026
    2026
  • Educational Ai for Attention-Enhanced Facial Emotion Recognition for Emotion-Aware Learning Systems Using Face-Cropped Deep Networks
    B Shannaq, M Alshar'e, A Abdullah, H Alsharu, O Ali
    Bangladesh Journal of Multidisciplinary Scientific Research 11 (2), 96-105 , 2026
    2026
  • DISCOVERING LEARNER PERSONAS IN AI-ASSISTED ENGLISH LANGUAGE LEARNING USING COSINE-BASED CLUSTERING: IMPLICATIONS FOR PERSONALIZED SUPPORT IN GCC CONTEXTS
    M Alshar'e, S ELAYYAN, abdallah abualkishik, K ABUHMAIDAN, ...
    Bangladesh Journal of Multidisciplinary Scientific Research 11 (2), 14/4/2026 , 2026
    2026
  • Elliptic curve cryptography based light weight technique for information security
    M Alsharâ, S Alzuâ, A Al-Haraizah, HA Alkhazaleh, M Jawarneh, ...
    Bulletin of Electrical Engineering and Informatics 14 (3), 2300-2308 , 2025
    2025
    Citations: 2
  • Classification of threats and countermeasures of cloud computing
    R Almanasir, D Al-solomon, S Indrawes, M Almaiah, U Islam, M Alshar'e
    Journal of Cyber Security and Risk Auditing 2025 (2), 27-42 , 2025
    2025
    Citations: 45
  • Adoption of Cloud-Based Smart Grids: Insights from Oman's Electricity Sector
    AAHO Abdallah M. Abualkishik, Khaled Abuhmaidan , Salem Salameh, Esraa ...
    Fusion: Practice and Applications 20 (2), 200-215 , 2025
    2025
  • Design and Development of SLRAI Wizards as an AI-Powered Solution to Enhance Learning Experiences in the Research Process
    RMKR Roslia, AF Zakariaa, AM Abualkishikc, M Alshar’ec
    Jurnal Kejuruteraan 37 (8), 3931-3942 , 2025
    2025
  • Empowering the Future: Digital Twins and Connectivity in Smart City Development
    A Abualkishik, M Alshar'e, A Kayed, K Abuhmaidan, A Odeibat
    5G Enabled Technology for Smart City and Urbanization System, 220-231 , 2025
    2025
  • Assessing blockchain's role in healthcare security: a comprehensive review
    M Alshar'e, K Abuhmaidan, FYH Ahmed, A Abualkishik, M Al-Bahri, ...
    Informatica 48 (22) , 2024
    2024
    Citations: 12
  • Enhancing Project Security: Unveiling Trust, Interpretability and Explainability in the Age of AI
    M Alshar'e, A Abualkishik, K Abuhmaidan, A Kayed
    5G Enabled Technology for Smart City and Urbanization System, 163-178 , 2024
    2024
    Citations: 9
  • Accurate prediction of chronic diseases using deep learning algorithms
    RS Cordova, RLR Maata, M Jawarneh, M Alshar'e, OC Agustin
    IAES International Journal of Artificial Intelligence (IJ-AI) 14 (1), 570-583 , 2024
    2024
  • Enhancing Data Protection in Digital Communication: A Novel Method of Combining Steganography and Encryption.
    KH Abuhmaidan, MA Al-Share, AM Abualkishik, A Kayed
    KSII Transactions on Internet & Information Systems 18 (6), 1619 , 2024
    2024
    Citations: 6
  • Enhancing IoT Network Security Through Digital Object Architecture-Based Approaches
    M Al-Bahri, W Alkishri, FYH Ahmed, M Alshar'e, S Al Maskari
    Qubahan Academic Journal 4 (1), 224-239 , 2024
    2024
    Citations: 7
  • Smart in-cabin air monitoring system using iot technologies
    FYH Ahmed, JH Yousif, M Alshar’e, M El Sheikh, E Al-Ajmi, M Al-Bahri
    Qubahan Academic Journal 4 (1), 78-90 , 2024
    2024
    Citations: 6
  • Intelligent Gesture Recognition System for Deaf People by using CNN and IoT.
    A Abualkishik, W Alzyadat, M Al Share, S Al-Khaifi, M Nazari
    International Journal of Advances in Soft Computing & Its Applications 15 (3) , 2023
    2023
    Citations: 13
  • An adaptive two-factor authentication scheme based on the usage of schnorr signcryption algorithm
    H Albazar, A Abdel-Wahab, M Alshar'e, A Abualkishik
    Informatica 47 (5) , 2023
    2023
    Citations: 12
  • Cyber security framework selection: Comparision of NIST and ISO27001
    M Alshar'e
    Applied computing Journal, 245-255 , 2023
    2023
    Citations: 67
  • The impact of virtual reality technology on Jordan’s learning environment and medical informatics among physicians
    M Jawarneh, M Alshar′ e, DA Dewi, M Al Nasar, R Almajed, A Ibrahim
    International Journal of Computer Games Technology 2023 (1), 1678226 , 2023
    2023
    Citations: 52
  • A Face Recognition Method In Machine Learning (ML) For Enhancing Security In Smart Home
    M Alshar'e, M Al Nasar, DVT R. Kumar, M. Sharma
    2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 10
  • Hybrid User Evaluation Methodology for Remote Evaluation: Case study of Educational games for children during Covid-19 Pandemic.
    M Alshar'e, A Albadi, M Mustafa, N Tahir, MA Amri
    Journal of Positive School Psychology 6 (3) , 2022
    2022
    Citations: 6

MOST CITED SCHOLAR PUBLICATIONS

  • Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems
    M Mustafa, M Alshare, D Bhargava, R Neware, B Singh, P Ngulube
    Computational and mathematical methods in medicine 2022 (1), 6112815 , 2022
    2022
    Citations: 82
  • Cyber security framework selection: Comparision of NIST and ISO27001
    M Alshar'e
    Applied computing Journal, 245-255 , 2023
    2023
    Citations: 67
  • The impact of virtual reality technology on Jordan’s learning environment and medical informatics among physicians
    M Jawarneh, M Alshar′ e, DA Dewi, M Al Nasar, R Almajed, A Ibrahim
    International Journal of Computer Games Technology 2023 (1), 1678226 , 2023
    2023
    Citations: 52
  • Classification of threats and countermeasures of cloud computing
    R Almanasir, D Al-solomon, S Indrawes, M Almaiah, U Islam, M Alshar'e
    Journal of Cyber Security and Risk Auditing 2025 (2), 27-42 , 2025
    2025
    Citations: 45
  • The moderating effect of demographic factors acceptance virtual reality learning in developing countries in the Middle East
    M Mustafa, S Alzubi, M Alshare
    International Conference on Advances in Computing and Data Sciences, 12-23 , 2020
    2020
    Citations: 26
  • Usability evaluation of educational games: an analysis of culture as a factor Affecting children’s educational attainment
    M Alshar’e, A Albadi, M Jawarneh, N Tahir, M Al Amri
    Advances in Human‐Computer Interaction 2022 (1), 9427405 , 2022
    2022
    Citations: 25
  • Evaluation of autistic children s education in Oman: the role of eLearning as a major aid to fill the gap.
    M Alshar'e, M Mustafa
    Elementary Education Online 20 (5), 5531-5531 , 2021
    2021
    Citations: 19
  • Managing and analyzing factors influencing Saudi college students’ entrepreneurial intention during the Covid-19 pandemic
    M Mustafa, M Alshar’e, A Shariah, M Al-Alawi, A Mohammad
    Turkish Journal of Physiotherapy and Rehabilitation 32 (3) , 2021
    2021
    Citations: 18
  • A user protection model for the trusted computing environment
    MI Alshar'e, R Sulaiman, MR Mukhtar, AM Zin
    Journal of Computer Science 10 (9), 1692 , 2014
    2014
    Citations: 16
  • Challenges and opportunities of using blockchain in supply chain management
    RS Cordova, RLR Maata, FJ Epoc, M Alshar'e
    Global Business and Management Research 13 (3), 204-217 , 2021
    2021
    Citations: 14
  • Intelligent Gesture Recognition System for Deaf People by using CNN and IoT.
    A Abualkishik, W Alzyadat, M Al Share, S Al-Khaifi, M Nazari
    International Journal of Advances in Soft Computing & Its Applications 15 (3) , 2023
    2023
    Citations: 13
  • Efficient statistical learning framework with applications to human activity and facial expression recognition
    F Najar, S Bourouis, M Alshar’e, R Alroobaea, N Bouguila, AH Al Badi, ...
    2020 5th international conference on advanced technologies for signal and … , 2020
    2020
    Citations: 13
  • EVALUATION OF THE TPM USER AUTHENTICATION MODEL FOR TRUSTED COMPUTERS.
    M ALSHAR'E, AM Zin, R Sulaiman, MR Mokhtar
    Journal of Theoretical & Applied Information Technology 81 (2) , 2015
    2015
    Citations: 13
  • Assessing blockchain's role in healthcare security: a comprehensive review
    M Alshar'e, K Abuhmaidan, FYH Ahmed, A Abualkishik, M Al-Bahri, ...
    Informatica 48 (22) , 2024
    2024
    Citations: 12
  • An adaptive two-factor authentication scheme based on the usage of schnorr signcryption algorithm
    H Albazar, A Abdel-Wahab, M Alshar'e, A Abualkishik
    Informatica 47 (5) , 2023
    2023
    Citations: 12
  • Design and Implementation of the TPM User Authentication Model.
    MI Alshar'e, R Sulaiman, MR Mokhtar, AM Zin
    J. Comput. Sci. 10 (11), 2299-2314 , 2014
    2014
    Citations: 12
  • A Framework of the Training Module for Untrained Observers in Usability Evaluation Motivated by COVID‐19: Enhancing the Validity of Usability Evaluation for Children’s …
    M Alshar’e, A Albadi, M Mustafa, N Tahir, M Al Amri
    Advances in Human‐Computer Interaction 2022 (1), 7527457 , 2022
    2022
    Citations: 11
  • A Face Recognition Method In Machine Learning (ML) For Enhancing Security In Smart Home
    M Alshar'e, M Al Nasar, DVT R. Kumar, M. Sharma
    2nd International Conference on Advance Computing and Innovative … , 2022
    2022
    Citations: 10
  • Enhancing Project Security: Unveiling Trust, Interpretability and Explainability in the Age of AI
    M Alshar'e, A Abualkishik, K Abuhmaidan, A Kayed
    5G Enabled Technology for Smart City and Urbanization System, 163-178 , 2024
    2024
    Citations: 9
  • Evaluation of E-Learning Method as a Mean to Support Autistic Children Learning in Oman
    M Alshar'e, M Mustafa, Q Bsoul
    Journal of Positive School Psychology 6 (3), 3040–3048 , 2022
    2022
    Citations: 8